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Field
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structure-preserving algorithms to generative modeling in AI. It will build upon the work done at IMF and SINTEF in this field. We will consider techniques like flow matching, and use ideas from optimal
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lipid fermentations by integrating data from online spectroscopy, standard bioreactor sensors, and lab-scale bioreactor experiments across various oleaginous microorganisms. The candidate will build and
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Inria, the French national research institute for the digital sciences | Talence, Aquitaine | France | 2 months ago
and refining their dynamic models through parameter identification on dedicated test benches, including external measurement systems (encoders, force sensors). The refined models will serve in
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novel algorithms, aiming for theoretical guarantees when working with structured and unstructured data. Pursue and complete a PhD thesis within the appointed 4-year duration. Join the Vienna Doctoral
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-frequency eddy current sensor validated against real ROT measurements; (3) a real-time physics-constrained state estimator integrating JMAK transformation kinetics with the EM forward model; and (4) a model
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advanced algorithms for solving complex optimization problems. You will work with real-world case studies to design future-proof supply chain networks for key agricultural products in vulnerable regions
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of artificial intelligence and consumer behavior. • Designing, developing, and evaluating artificial intelligence (AI)-based techniques and algorithms related to consumer behavior and manipulative online
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sensors in daily life. By linking physiological markers of fatigue to movement quality and real-world activity, you will gain new insights into why mobility deteriorates during daily activities and how
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: Transparency. As part of this doctoral project, you will develop novel knowledge representation techniques, algorithms for human-in-the-loop optimization, and interactive tools that combine graphical and natural
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-constrained devices such as wearables, smart sensors, hearables, and IoT nodes. While current deployment methodologies can optimize models before deployment, the resulting software remains static throughout